This chapter provides an overview of the functionality and the underlying principles of the brain-computer interfaces (BCI) developed by the Chair in Non-Invasive Brain-Machine Interface (CNBI) of the Swiss Federal Institute of Technology (EPFL), as well as exemplary applications where those have been successfully evaluated. Our laboratory mainly develops \emph{non-invasive} BCI systems based on electroencephalographic (EEG) signals and, thus, devoid of medical hazards, real-time, portable, relatively low-cost and minimally obtrusive. Our research is pushing forward \emph{asynchronous} paradigms offering a spontaneous, user-driven and largely ecological interaction. Furthermore, we stand on the \emph{machine-learning} way to BCI with emphasis on personalization, configurability and adaptability, coupled with \emph{mutual learning} training protocols, so that elaborate signal processing and pattern recognition methods are optimally combined with the user's learnable modulation of brain signals towards high and robust performances and universal usability. Additionally, \emph{cognitive mental state} monitoring is employed to shape or refine the interaction. \emph{Shared-control} approaches allow smart, context-aware robotics to complement the BCI channel for more fine-grained control and reduction of the user's mental workload. Last but not least, \emph{hybrid BCI} designs exploit additional physiological signals to augment the BCI modality and enrich the control paradigm, thus also exploiting potential residual capabilities of disabled end-users. The applicability and effectiveness of the aforementioned principles is hereby demonstrated in four exemplary applications evaluated with both able-bodied and motor-disabled end-users. These applications include a hybrid, motor imagery (MI)-based speller, a telepresence robot equipped with shared-control, cognitive mental state monitoring paradigms able to recognize and correct errors, and, finally, a car driving application where a passive BCI enabled on a smart car assists towards increased safety and improved driving experience. Remarkably, our results show that the performance of end-users with disabilities was similar to that of a group of healthy users, who were more familiar with the experiment and the environment. This demonstrates that end-users are able to successfully use BCI technology.